Optimizing Tabular Foundation Model Inference: Integrating TabPFNv2 for Zero-Shot Classification
By utilizing TabPFN-2.5 distillation engines to convert Transformers into MLPs or tree ensembles, engineers can reduce inference latency by orders-of-magnitude while maintaining SOTA zero-shot classification performance, provided they manage the memory footprint constraints inherent in H100-class deployments.
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